Inhomogeneity caused by non-tissue characteristics is an undesired and unavoidable artifact, which often adversely affects intensity-based qualitative and quantitative MR image analysis. It becomes more severe in higher magnetic fields and for objects with higher permittivity where the wavelength of the RF field in the objects is comparable with or less than the size of the objects. In MR imaging, imperfections of receiver and transmit coils, static magnetic field inhomogeneity, radio frequency (RF) penetration, gradient-driven eddy currents, and object-dependent electromagnetic interactions systematically cause the variations of signal intensities across images which is also referred as bias field, intensity non-uniformity, or shading. MR signal inhomogeneity presents a major challenge for magnetic resonance imaging (MRI) and magnetic resonance spectroscopy (MRS). More specifically, even subtle signal inhomogeneity can cause large degradation on image quality, which must be discounted by physicians when viewing images and therefore often obstructs diagnoses and causes treatment delays. Moreover, artificial intra-tissue variability caused by signal inhomogeneity affects automated image processing algorithms that rely on the assumption that a given tissue is represented by similar voxel intensities throughout the image. Subsequently, it reduces the accuracy of quantitative analyses and limits the detection sensitivity of computer-aided diagnosis. Finally, signal inhomogeneity increases inter-scanner variability. MR images acquired with similar protocols but on different scanners may generate dissimilar image intensities for the same tissue types due to different coil configurations and coupling between the coil and the to-be-imaged objects. The variabilities across different sites and time points in longitudinal studies are machine-dependant, and go beyond random or systematic errors that can be corrected. As a result, the number of required subjects have to be increased to improve statistical power. For example, to have statistically reliable and significant results, the Alzheimer's Disease Neuroimaging Initiative (ADNI) have spent $60-million over 5 years imaging and tracking more than 800 subjects.
Before the commencement of each MR scan, it is common practice to adjust the strength of the transmitted RF excitation field and the gain of the RF receiver to ensure that the RF excitation pulses have optimal frequency, strength, and duration to evoke the desired MR signal. This does not necessarily mean that the expected RF excitation field will be produced uniformly throughout the region of interest, or that the resulting MR signals will be received uniformly from all locations in the region of interest. RF field produced by most transmit coils after loading of the subject being studied is not homogeneous and the receive field of most receiver coils is also not homogeneous. This is particularly true for imperfect coil configurations, such as surface coil and phase array coils. Even if the transmit and receiver coil fields are homogeneous for free space or in the unloaded condition, wave behavior and penetration of the RF field into the subject may give rise to non-uniform transmit field and receiver sensitivity throughout the region of interest. Moreover, incorrect calibration of the RF pulse amplitude, instability or drift of the RF amplifier or other RF electronics, can lead to non-uniform transmit field. Also, mutual inductance between transmit and receiver coils may cause further inhomogeneities. Either inhomogeneous transmit or receiver sensitivity or both can give rise to ghost artifacts in signal intensity, and therefore restrict the application of MR techniques in research and clinical settings.
Methods for correcting MR signal inhomogeneities can be categorized into active and post-processing methods. The active methods are achieved through the applications of adiabatic pulses, compensation pulses, radiofrequency field shimming techniques, and parallel transmit techniques. Most of these active methods concentrate on the correction of signal inhomogeneity caused by transmit coils; while only parallel transmit techniques partially correct inhomogeneity caused by receiver coils. The post-process methods can be further classified into model-based (e.g. low-pass filtering, statistical modeling and surface fitting) and measurement-based methods (U.S. Pat. No. 6,757,442, Ainash 2004 from GE Medical Systems Global Technology Company; U.S. Pat. No. 7,218,107, Fuderer 2007 from Koninklijke Philips Electronics N.V.; U.S. Pat. No. 7,672,498, Jellus 2010 from Siemens Aktiengsellschaft; U.S. Pat. No. 7,894,668, Boitano 2011 and U.S. Pat. No. 8,213,715, Boitano 2012 from Fonar Corporation). Most model-based methods are usually established on the assumption that MR signal inhomogeneity changes slowly and smoothly. Since the configurations of the objects being imaged are very complex, the assumption is sometimes not valid. Moreover, the model-based methods usually do not consider the influence of image acquisition and the imaged object on the inhomogeneities. These methods require some huge initial effort and extensive skills to select the right model and correct setting. Measurement-based methods, on the other hand, inclusively incorporate prior knowledge about factors that affect signal inhomogeneity into the correction. [Brey W W, Narayana P A. 1988; Murakami J W et al. 1996; Liney G P et al. 1998, U.S. Pat. No. US2012/0032677 A1 Dannels (2012) from Toshiba Medical System Corporation].
A number of methods have been proposed for estimating transmit field in vivo. These methods can be categorized into MR amplitude based and M R phase based methods. M R amplitude based methods include the double flip angle method [Insko E K et al, 1993; Cunningham C H et al 2006], dual pulse spin echo method [Jiru F et al, 2006], actual flip angle imaging method [Yarnykh V L, 2007], and stead state method [Brunner et al, 2009]. M R phase based methods include Bloch Siegert shift method [Sacolick et al. 2010], and phase method [Morell D G 2008; Chang Y V, 2012]. Various methods have also been proposed for estimating receiver sensitivity in vivo. These methods can be categorized into intensity-based, field-based and k-space calibration methods. Intensity-based methods include the pre-scan method (Pruessmann et al, 1999), minimal contrast method (Wang J et al, 2005a and 2005b), and uniform magnetization method (Dai W et al, 2011). Field-based methods include the reciprocity principle method (Hoult D I et al, 1976), rotating-object method (Wang J et al, 2009), calibration from transmit field (Watanabe H, 2011), and electromagnetic field method (Wang J et al 2013, US 20130251227 A1). Because coil sensitivity varies slowly and smoothly over space, the k-space calibration methods have also been used to estimate receiver sensitivity for parallel imaging reconstruction (Griswold M A et al, 2006; Breuer F A et al, 2005; McKenzie C A et al, 2007).
Thus, signal intensity inhomogeneity correction is a challenging problem involving multiple communities with different objectives. Performance evaluation is a consideration for the investigation of consistency among methods as well as for the optimization of existing and development of novel correction methods. Due to the lack of ground-truth, direct evaluation using experimentally collected human subject MR data is not feasible. The most commonly used evaluation is based on computer simulations. However, because it is difficult to describe MR scanner procedures exactly, most existing simulation based performance evaluation methods yield poor validity scores and often lead to conflicting statements. In the present disclosure, phantom and in vivo experiments are used to evaluate the performance of various signal inhomogeneity correction methods.